Clinical Practice Guidelines & Communicating Risk

Adam La Caze

Semester 1, 2023

Objectives

  • Be able to describe the development and key characteristics of clinical practice guidelines

  • Be able to critically appraise clinical practice guidelines

  • Be able to describe the challenges of risk communication

Key concepts

  • Guidelines need to synthesise and interpret evidence to make actionable recommendations

  • Guidelines need to be critically appraised and implemented with care—it is often useful to know the evidence underlying the recommendations

  • People are frequently confused by numbers

  • Use natural frequencies (‘5 out of 100’), provide benefits and harms, and use decision-aids

Clinical Practice Guidelines

Key characteristics

  • Developed by an expert panel

  • Often published by government bodies or professional bodies (e.g. NHMRC, National Asthma Council, Royal Australian College of General Practitioners, etc)

  • Compile and synthesise available research and clinical experience into actionable recommendations

  • Typically focus on a particular condition or clinical area (e.g. management of acute coronary syndrome, treatment of osteoarthritis, etc)

NHMRC Standards for Guidelines National Health and Medical Research Council (NHMRC) (2016)

Guideline developers will:

  1. Be relevant and useful for decision making

  2. Be transparent in process of development, sources of evidence and funding

  3. Ensure the development group has the right mix of skills and include end-users

  1. Identify and manage conflicts of interest

  2. Be evidence-informed: comprehensive search, explicit criteria for assessing evidence and making recommendations

  3. Make actionable recommendations

  4. Be up-to-date (and plan for review)

  5. Be accessible

Guidelines provide guidance

  • The guideline writing committee needs to appraise the relevant evidence and then make a concrete recommendation regarding best practice

  • Recommendations go beyond the evidence—among other things, guideline writers need to consider context, values and cost

  • The guideline writing committee play an important role in formulating recommendations

  • This emphasises the importance of clear criteria for making recommendations and addressing conflicts of interest

Evidence and Recommendations

GRADE (Guyatt, Oxman, Vist, et al., 2008)

  • Criteria for recommendations in systematic reviews and guidelines

  • Separates quality of evidence and strength of recommendation

Quality of evidence Strength of recommendation
High Strong
Moderate Weak
Low
Very low
  • Focusses on endpoints (rather than studies)

GRADE: Quality of evidence

The advice is very similar to applying the EBM hierarchy of evidence. Criteria are provided for shifting quality of evidence rating up or down, including:

Although observational studies … start with a “low quality” rating, grading upwards may be warranted if the magnitude of the treatment effects is very large (such as severe hip osteoarthritis and hip replacement), if there is evidence of a dose-response relation or if all plausible biases would decrease the magnitude of an apparent treatment effect

Guyatt, Oxman, Vist, et al. (2008, p. 926)

Further considerations regarding quality of evidence

  • Study limitations (e.g. risk of bias tools)
  • Inconsistency of results (especially, unexplained inconsistency)
  • Indirectness of evidence (e.g. different population, intervention or context)
  • Imprecision (e.g. wide confidence intervals)
  • Reporting bias

GRADE: Strength of recommendation

Determinants of strength of recommendation (Guyatt, Oxman, Kunz, et al., 2008):

  • Balance between desirable and undesirable effects

  • Quality of evidence

  • Values and preferences

  • Costs (resource allocation)

  • You can have a strong recommendation for treatment based on low quality evidence

    • Low quality evidence for a benefit in a life-threatening situation in which the risk of harms is low
  • You can frequently have a weak recommendation for treatment based on high quality evidence

    • High quality of evidence for benefit but there is a close trade-off between benefits and harms or there is wide variability in values

Example: Australian clinical guidelines for the management of acute coronary syndromes 2016

  • Clear statement of intended use: improving the quality of care for patients with non-ST elevation acute coronary syndrome

  • Criteria for assessing evidence and providing recommendations

  • Clearly described policies regarding conflict of interest (but the link is not longer available!)

Guidance regarding dual antiplatelet therapy (Chew et al., 2016)

Pharmacology for ACS (selected)
Recommendation Strength of Recommendation
Aspirin 300 mg orally initially (dissolved or chewed) followed by 100–150 mg/day is recommended for all patients with ACS in the absence of hypersensitivity. Strong (IA)
Among patients with confirmed ACS at intermediate to very high- risk of recurrent ischaemic events, use of a P2Y12 inhibitor (ticagrelor 180 mg orally, then 90 mg twice a day or; prasugrel 60 mg orally, then 10 mg daily; or clopidogrel 300–600 mg orally, then 75mg per day) is recommended in addition to aspirin. (Ticagrelor or prasugrel preferred: see practice advice) Strong (IA)
Discharge management and secondary prevention (selected)
Recommendation Strength of Recommendation
Aspirin (100–150 mg/day) should be continued indefinitely unless it is not tolerated or an indication for anticoagulation becomes apparent. Strong (IA)
Clopidogrel should be prescribed if aspirin is contraindicated or not tolerated. Strong (IA)
Dual-antiplatelet therapy with aspirin and a P2Y12 inhibitor (clopidogrel or ticagrelor) should be prescribed for up to 12 months in patients with ACS, regardless of whether coronary revascularisation was performed. Strong (IA)
Consider continuation of dual-antiplatelet therapy beyond 12 months if ischaemic risks outweigh the bleeding risk of P2Y12 inhibitor therapy; conversely consider discontinuation if bleeding risk outweighs ischaemic risks. Weak (IIC)

Limitations of guidelines

  • Susan, 68 years, has heart failure, hypertension, atrial fibrillation, type 2 diabetes and depression
  • What are her guideline-recommended medicines?
  • Susan sees her GP twice a week (on average), a cardiologist once a month, endocrinologist every 6 weeks and a psychologist once a month
  • Susan is in the pharmacy every week and spends approximately one hour every day looking after her medicines
  • She cares for her husband who is the ‘sick’ one in the family

Multimorbidity is the norm

(ref:cap)

Single-disease guidelines in patients with multimorbidity

Treatment according to single-disease guidelines is likely to:

  • Give Susan a significant treatment burden

  • Under-estimate the harms of combined treatments (drug-disease interactions, drug-drug interactions)

  • Provide limited guidance on when treatments can be stopped: de-prescribing

Guidelines need to be implemented with care and common sense

Responses to the challenge of single-disease guidelines

Minimally disruptive medicine (MDM) is a patient-centered approach to care that focuses on achieving patient goals for life and health while imposing the smallest possible treatment burden on patients’ lives. It is particularly appropriate for patients who are at risk of being (or who already are) overwhelmed by the demands of life, illness, and health care.

Leppin et al. (2015, p. 51)

Industry influence on guidelines

  • Conflicts of interests are common on clinical practice guidelines

  • There is limited data showing that conflicts of interest influence recommendations

  • But there are case studies of clear industry influence (Lenzer, 2013): high-dose steroids for acute spinal injury; exaggerated claims regarding alteplase for stroke

Industry bias (Funding bias, Sponsorship bias)

The tendency of a scientific study to support the interests of the study’s financial sponsor Wikipedia

Industry bias(Lundh et al., 2017)

  • Lundh et al. (2017) conducted a meta-analysis of reports of studies that assessed samples of papers for industry bias

  • Industry sponsored studies more often had favourable efficacy results (RR 1.27, 95% CI 1.17–1.37)

  • This effect is present despite industry sponsored studies possessing similar (or lower) risk of bias when compared to non-industry sponsored studies.

Responding to industry influence

  • Transparency: clear reporting of conflicts of interest

  • Beware guidelines funded by sponsors

Communicating Risk

An example (Tversky & Kahneman, 1983)

Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Which is more probable?

  1. Linda is a bank teller

  2. Linda is a bank teller and is active in the feminist movement

Communicating risk (Ahmed et al., 2012; Edwards, 2002)

  • Qualitative descriptors of risk are interpreted differently: ‘high risk’, ‘low risk’,…

  • Numbers presented in different ways confuse people: which is worse, a death rate of 1286 out of 10,000 or a death rate of 24.14 out of 100?

  • Framing is important (and its effects depend on context): increasing survival or reducing death?

  • Both absolute and relative risks can be important, and each in isolation can be misleading; patients find NNT confusing

  • Need more work on the best way to numerically communicate uncertainty

Pictographs

From Ahmed et al. (2012)

Advice for communicating risk (Hoffmann et al., 2017, Ch. 14)

  • Communicate uncertainty
  • Present natural frequencies where possible (e.g. ‘3 out of 100 people’)
  • Use natural frequencies for comparison rather than relative risk or NNT (if possible)
  • Give benefits and harms using the same denominator
  • Clearly define the time scale
  • Use decision aids
  • Use multiple formats
  • Try to put risk into perspective by comparing it to other events
  • Less is more: avoid overload

References

Ahmed, H., Naik, G., Willoughby, H., & Edwards, A. G. K. (2012). Communicating risk. British Medical Journal, 344(jun18 1), e3996–e3996. https://doi.org/10.1136/bmj.e3996
Chew, D. P., Scott, I. A., Cullen, L., French, J., Briffa, T. G., Tideman, P. A., Woodruffe, S., Kerr, A., Branagan, M., & Aylward, P. E. (2016). National Heart Foundation of Australia & Cardiac Society of Australia and New Zealand: Australian Clinical Guidelines for the Management of Acute Coronary Syndromes 2016. Heart Lung and Circulation, 25(9), 895–951.
Edwards, A. (2002). Explaining risks: turning numerical data into meaningful pictures. BMJ (Clinical Research Ed), 324(7341), 827–830. https://doi.org/10.1136/bmj.324.7341.827
Guthrie, B., Thompson, A., Dumbreck, S., Flynn, A., Alderson, P., Nairn, M., Treweek, S., & Payne, K. (2017). Better guidelines for better care: accounting for multimorbidity in clinical guidelines – structured examination of exemplar guidelines and health economic modelling. Health Services and Delivery Research, 5(16), 1–150. https://doi.org/10.3310/hsdr05160
Guyatt, G. H., Oxman, A. D., Kunz, R., Falck-Ytter, Y., Vist, G. E., Liberati, A., & Schunemann, H. J. (2008). Going from evidence to recommendations. BMJ (Clinical Research Ed), 336(7652), 1049–1051. https://doi.org/10.1136/bmj.39493.646875.AE
Guyatt, G. H., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schunemann, H. J. (2008). GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 336(7650), 924–926. https://doi.org/10.1136/bmj.39489.470347.AD
Hoffmann, T., Bennett, S., & Del Mar, C. (2017). Evidence-based practice across the health professions / Tammy Hoffmann, Sally Bennett, Chris Del Mar. (3rd editio). Elsevier Australia a division of Reed International Books Australia Pty Ltd.,.
Lenzer, J. (2013). Why we can’t trust clinical guidelines. BMJ (Online), 346(7913), 1–5. https://doi.org/10.1136/bmj.f3830
Leppin, A., Montori, V., & Gionfriddo, M. (2015). Minimally Disruptive Medicine: A Pragmatically Comprehensive Model for Delivering Care to Patients with Multiple Chronic Conditions. Healthcare, 3(1), 50–63. https://doi.org/10.3390/healthcare3010050
Lundh, A., Lexchin, J., Mintzes, B., Jb, S., & Bero, L. (2017). Industry sponsorship and research outcome (Review). Cochrane Database of Systematic Reviews, 2. https://doi.org/10.1002/14651858.MR000033.pub3.www.cochranelibrary.com
National Health and Medical Research Council (NHMRC). (2016). 2016 NHMRC Standards for Guidelines. https://www.nhmrc.gov.au/guidelines-publications/information-guideline-developers/2016-nhmrc-standards-guidelines
Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review, 90(4), 293. http://psycnet.apa.org/journals/rev/90/4/293/